We used the entire 3D lung scan with both the right and left lungs to identify clusters within our images. We used a similar CAE as in our simulations, but changed the 2D convolutional layers to 3D and reduced the second convolutional layer from a stride length of 1 to 2, to reduce the number of learned parameters which can become very large in the 3D space; then, the 3D input image was of dimension \((88\times 88\times 88)\) , with the output of the last convolutional layer at \((11\times 11\times 11)\) . We estimated the optimal number of clusters prior to the clustering layer of our framework using the Silhouette method [1]} with sparse k-means [2]}. Then, setting the number of clusters to the optimal estimate, we identified clusters within these data, using CLAM to localize the discriminative regions. To evaluate the usefulness of the new subtypes identified with our clustering methodology, we found the linear associations with our clusters and various clinical outcomes. Clinical outcomes included lung function using the forced expiratory volume at one second (FEV1), forced vital capacity (FVC), diffusing capacity for carbon monoxide (DLCO), laboratory testing including CD4 cell count and C-Reactive protein (CRP), and patient reported outcomes, including fatigue assessment scale (FAS), gastroesophageal reflux disease questionnaire (GERDQ), cognitive failure questionnaire (CFQ), shortness of breath questionnaire (SOBQ), Promis, and SF-12 questionnaire.
